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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Internet Technologies for Space-based Communications: State of the Art and Challenges

The Internet is rapidly changing the ways we communicate information around the globe today. The desire to provide Internet-based services to anyone, anywhere, anytime has brought satellite communications to the forefront to become an integral part of the Internet. In spite of the distances involved, satellite links are proving to be capable of providing Internet services based on Internet protocol (TCP/IP) stack. This development has led to the question particularly at NASA; can satellites and other space platforms become an Internet-node in space? This will allow the direct transfer of information directly from space to the users on Earth and even be able to control the spacecraft and its instruments. NASA even wants to extend the near earth space Internet to deep space applications where scientists and the public here on Earth may view space exploration in real time via the Internet. NASA's future solar system exploration will involve intensive in situ investigations of planets, moons, asteroids, and comets. While past missions typically involved a single fly-by or orbiting science spacecraft, future missions will begin to use fleets of small, highly intelligent robotic vehicles to carry out collaborative investigations. The resulting multi-spacecraft topologies will effectively create a wide area network spanning the solar system. However, this will require significant development in Internet technologies for space use. This paper provides the status'of the Internet for near earth applications and the potential extension of the Internet for use in deep space planetary exploration. The paper will discuss the overall challenges of implementing the space Internet and how the space Internet will integrate into the complex terrestrial systems those forms the Internet of today in a hybrid set of networks. Internet. We envision extending to the deep space environment such Internet concepts as a well-designed layered architecture. This effort will require an ability to develop and infuse new physical layer technology to increase network bandwidth at very low-bit error rates. In addition, we identify network technologies such as routers and switches needed to maintain standard application layer interfaces, while providing low-cost, efficient, modular networking solutions. We will describe the overall architectural approach to extending the concept of the Internet to space and highlight the important technological challenges and initiatives that will make it a reality.

Bhasin, K.↗
DOE OSTI2014

Multidimensional resonance raman spectroscopy by six-wave mixing in the deep UV

Two-dimensional (2D) resonance Raman spectroscopies hold great potential for uncovering photoinduced relaxation processes in molecules but are not yet widely applied because of technical challenges. Here, we describe a newly developed 2D resonance Raman experiment operational at the third-harmonic of a Titanium-Sapphire laser. High-sensitivity and rapid data acquisition are achieved by combining spectral interferometry with a background-free (six-pulse) laser beam geometry. The third-harmonic laser pulses are generated in a filament produced by the fundamental and second-harmonic pulses in neon gas at pressures up to 35 atm. The capabilities of the setup are demonstrated by probing ground-state wavepacket motions in triiodide. The information provided by the experiment is explored with two different representations of the signal. In one representation, Fourier transforms are carried out with respect to the two experimentally controlled delay times to obtain a 2D Raman spectrum. Further insights are derived in a second representation by dispersing the signal pulse in a spectrometer. It is shown that, as in traditional pump-probe experiments, the six-wave mixing signal spectrum encodes the wavepacket's position by way of the (time-evolving) emission frequency. Anharmonicity additionally induces dynamics in the vibrational resonance frequency. In all cases, the experimental signals are compared to model calculations based on a cumulant expansion approach. This study suggests that multi-dimensional resonance Raman spectroscopies conducted on systems with Franck-Condon active modes are fairly immune to many of the technical issues that challenge off-resonant 2D Raman spectroscopies (e.g., third-order cascades) and photon-echo experiments in the deep UV (e.g., coherence spikes). The development of higher-order nonlinear spectroscopies operational in the deep UV is motivated by studies of biological systems and elementary organic photochemistries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2001

The Evolution of the Martian Hydrosphere: Implications for the Fate of a Primordial Ocean and the Current State of the Northern Plains

In this paper we consider the hydraulic and thermal conditions that gave rise to the elevated source regions of the Late Hesperian outflow channels and explore their implications for the evolution of the Martian hydrosphere. We find that if the outflow channel floodwaters were derived from a subpermafrost aquifer, then it implies that, throughout the planet's first billion years of evolution, as much as one third of its surface was covered by standing bodies of water and ice. Following the development of the global dichotomy, the bulk of this water would have existed as an ice-covered ocean in the northern plains. We demonstrate that the progressive crustal assimilation of this early surface reservoir of H2O (punctuated by possible episodes of less extensive flooding) was a natural consequence of the planet's subsequent climatic and geothermal evolution-potentially cycling the equivalent of a km-deep global ocean of water through the atmosphere and subsurface every approx. 10(exp 9) years. In response to the long-term decline in planetary heat flow, the progressive coldtrapping of H2O into the growing cryosphere is expected to have significantly depleted the original inventory of groundwater-a development that could well explain the apparent decline in outflow channel activity observed during the Amazonian. Although primarily a theoretical analysis, our findings appear remarkably consistent with the geomorphic and topographic evidence that Mars once possessed a primordial ocean and that a substantial relic of that body continues to survive as massive ice deposits within the northern plains. Confirmation of the presence of such deposits, combined with the potential detection of a global-scale groundwater system, would provide persuasive support for the validity of this analysis.

Clifford, Stephen M.↗
DOE OSTI2018

Low-dose x-ray tomography through a deep convolutional neural network

Synchrotron-based X-ray tomography offers the potential of rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short exposure time projections enhanced with CNN show similar signal to noise ratios as compared with long exposure time projections and much lower noise and more structural information than low-dose fats acquisition without CNN. We optimized this approach using simulated samples and further validated on experimental nano-computed tomography data of radiation sensitive mouse brains acquired with a transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in datasets collected with low dose-CNN. As a result, this method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2006

Future Mission Trends and their Implications for the Deep Space Network

Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.

future mission needs↗
DOE OSTI2025

Considerations for the Global Commercialization of Floating Offshore Wind Energy

Floating offshore wind (FOW) has the potential to unlock access to wind resources in deep water where fixed-bottom turbines are not feasible, enabling coastal regions around the world to meet growing energy demands. Although fixed-bottom offshore wind is commercially mature, FOW, which may be needed for water deeper than 60 m, must progress in multiple ways to reach full commercial viability. In this Perspective, we examine the status of the global FOW industry's commercial development across three key areas - technical innovation, industrialization and cross-cutting value. Technical innovation has enabled FOW turbines to perform as well as fixed-bottom turbines, with the promise of future cost reductions. However, the complex architecture of FOW turbines, combining floating structures with more than 8,000 electrical and mechanical parts in wind turbines, requires industrialization efforts such as standardization and supply-chain integration to enable commercial project deployment. FOW can potentially offer unique benefits, including reduced environmental impacts and strengthened economic development in coastal regions, through substantial regional economic activity. Successful coordination across these three areas could help to position FOW as a major contributor to a competitive, reliable and resilient global energy system.

17 WIND ENERGY↗
DOE OSTI2019

Room-temperature stability of excitons and transverse-electric polarized deep-ultraviolet luminescence in atomically thin GaN quantum wells

Here, we apply first-principles calculations to study the effects of extreme quantum confinement on the electronic, excitonic, and radiative properties of atomically thin (1–4 atomic monolayers) GaN quantum wells embedded in AlN. We determine the quasiparticle bandgaps, exciton energies and wave functions, radiative lifetimes, and Mott critical densities as a function of well and barrier thickness. Our results show that quantum confinement in GaN monolayers increases the bandgap up to 5.44 eV and the exciton binding energy up to 215 meV, indicating the thermal stability of excitons at room temperature. Exciton radiative lifetimes range from 1 to 3 ns at room temperature, while the Mott critical density for exciton dissociation is approximately 10 13 cm -2 . The luminescence is transverse-electric polarized, which facilitates light extraction from c-plane heterostructures. We also introduce a simple approximate model for calculating the exciton radiative lifetime based on the free-carrier bimolecular radiative recombination coefficient and the exciton radius, which agrees well with our results obtained with the Bethe–Salpeter equation predictions. In conclusion, our results demonstrate that atomically thin GaN quantum wells exhibit stable excitons at room temperature for potential applications in efficient light emitters in the deep ultraviolet as well as room-temperature excitonic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Hydrological niche segregation defines forest structure and drought tolerance strategies in a seasonal Amazon forest

The relationship between rooting depth and above-ground hydraulic traits can potentially define drought resistance strategies that are important in determining species distribution and coexistence in seasonal tropical forests, and understanding this is important for predicting the effects of future climate change in these ecosystems. We assessed the rooting depth of 12 dominant tree species (representing c. 42% of the forest basal area) in a seasonal Amazon forest using the stable isotope ratios (δ 18 O and δ 2 H) of water collected from tree xylem and soils from a range of depths. We took advantage of a major ENSO-related drought in 2015/2016 that caused substantial evaporative isotope enrichment in the soil and revealed water use strategies of each species under extreme conditions. We measured the minimum dry season leaf water potential both in a normal year (2014; Ψ non-ENSO ) and in an extreme drought year (2015; Ψ ENSO ). Furthermore, we measured xylem hydraulic traits that indicate water potential thresholds trees tolerate without risking hydraulic failure (P 50 and P 88 ). We demonstrate that coexisting trees are largely segregated along a single hydrological niche axis defined by root depth differences, access to light and tolerance of low water potential. These differences in rooting depth were strongly related to tree size; diameter at breast height (DBH) explained 72% of the variation in the δ 18 Oxylem. Additionally, δ 18 O xylem explained 49% of the variation in P50 and 70% of P88, with shallow-rooted species more tolerant of low water potentials, while δ 18 O of xylem water explained 47% and 77% of the variation of minimum Ψ non-ENSO and Ψ ENSO . We propose a new formulation to estimate an effective functional rooting depth, i.e. the likely soil depth from which roots can sustain water uptake for physiological functions, using DBH as predictor of root depth at this site. Based on these estimates, we conclude that rooting depth varies systematically across the most abundant families, genera and species at the Tapajós forest, and that understorey species in particular are limited to shallow rooting depths. Our results support the theory of hydrological niche segregation and its underlying trade-off related to drought resistance, which also affect the dominance structure of trees in this seasonal eastern Amazon forest. Synthesis. Our results support the theory of hydrological niche segregation and demonstrate its underlying trade-off related to drought resistance (access to deep water vs. tolerance of very low water potentials). We found that the single hydrological axis defining water use traits was strongly related to tree size, and infer that periodic extreme droughts influence community composition and the dominance structure of trees in this seasonal eastern Amazon forest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. Here, the analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Dual-Biometric Human Identification Using Radar Deep Transfer Learning

Accurate human identification using radar has a variety of potential applications, such as surveillance, access control and security checkpoints. Nevertheless, radar-based human identification has been limited to a few motion-based biometrics that are solely reliant on micro-Doppler signatures. This paper proposes for the first time the use of combined radar-based heart sound and gait signals as biometrics for human identification. The proposed methodology starts by converting the extracted biometric signatures collected from 18 subjects to images, and then an image augmentation technique is applied and the deep transfer learning is used to classify each subject. A validation accuracy of 58.7% and 96% is reported for the heart sound and gait biometrics, respectively. Next, the identification results of the two biometrics are combined using the joint probability mass function (PMF) method to report a 98% identification accuracy. To the best of our knowledge, this is the highest reported in the literature to date. Lastly, the trained networks are tested in an actual scenario while being used in an office access control platform to identify different human subjects. We report an accuracy of 76.25%.

47 OTHER INSTRUMENTATION↗
NASA NTRSDate not supplied

Jarosite Formation in Deep Antarctic Ice, an Earth Analogue to Unveil the Origin of Jarosite on Mars?

On Earth, jarosite is a weathering product forming in acidic-oxidative environments from the alteration of iron-bearing minerals in presence of liquid water. Typical settings where this iron-potassium hydrated sulphate is found, are weathering zones of pyrite-rich deposits, evaporative basins and fumaroles. Jarosite is not only known on Earth, it also occurs on Mars, where it has been identified by the Opportunity rover. The mineral was in fact recognized in the finely layered formations outcropping at Meridiani Planum and that were accurately investigated by the rover (Klingelhöfer et al. 2004). Since jarosite requires liquid water to form, its occurrence on Mars has been regarded as an evidence for the occurrence of liquid water in the geologic past of Mars (Elwood-Madden et al., 2004). Since then, many models have been proposed to describe the environments where the precipitation of Martian jarosite took place. The most accepted ones deal with evaporative basins similar to Earth’s playas, others concern volcanic activity and hydrothermal processes. An alternative proposal predicted that jarosite may have formed as a consequence of weathering of mineral dust trapped in massive ice deposits, i.e. the ice-weathering model (Niles & Michalsky, 2009). The hypothesis that jarosite formed on Mars because of low-temperature, acidic and water limited weathering, is not new (Burns, 1987), but until now no direct evidences were available to support it.A potential Earth analogue to investigate such processes is deep Antarctic ice. We present a first investigation of deep ice samples from the Talos Dome ice core (East Antarctica) aimed at the identification of englacial jarosite, so as to support the ice-weathering model. Evidences gathered through independent techniques showed that jarosite is actually present in deep Antarctic ice and results from the weathering of dust trapped into ice. The process is controlled by the re-crystallization of ice grains and the concurrent re-location of impurities at grain-junctions, which both depend on ice depth. This study demonstrates that the deep englacial environment is suitable for jarosite precipitation. Our findings support the hypothesis that, as originally predicted by the ice-weathering model, paleo ice-related processes have been important in thegeologic and geochemical history of Mars.

Lindsay P Keller↗
DOE OSTI2022

Learning in continuous action space for developing high dimensional potential energy models

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗
DOE OSTI2023

Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models [Slides]

Machine learning yields great potentials in improving imaging performance (i.e., accuracy and efficiency). The incorporation of governing equation will improve generalization and alleviate label scarcity. Employ physical properties can reduce model complexity and significantly save training cost without compromising accuracy. Combining SciML imaging and edge computing would allow broader applications in energy, medicine, and other domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Programs Enabling Deep Technology Transfer from National Labs

To maximize the technology transfer potential, it is important to create an ecosystem for the inventors to adapt the technologies developed for basic science to successful commercial ventures. In this white paper we present a brief overview of technology transfer programs at high energy physics (HEP) laboratories with a focus on the programs at Fermilab and KEK, and identify opportunities and recommendations for increasing partnerships and commercialization at HEP-centric laboratories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRSDate not supplied

Language Model For Earth Science: Exploring Potential Downstream Applications As Well As Current Challenges

The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, auto-mated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data such as journal and conference papers, little efforts have focused on harnessing NLTs for knowledge extraction and supporting the scientific process. This paper surveys the use of language models in different science. BERT-E, a new Earth science-specific language model, is presented. BERT-E is generated using a transfer learning solution. A language model that has already been trained for general Science (SciBERT) is fine-tuned using abstracts and full text extracted from various Earth science-related articles. A downstream keywords classification application is used for evaluation, and the use of BERT-E shows improved performance. The need to develop a robust set of benchmarks in evaluating the language model such as BERT-E is discussed. Finally, example applications are presented to inspire additional ideas for applications using domain-specific language models.

R Ramachandran↗
NASA NTRS2014

Inferred Variable FeO Content in Medium-sized Lunar Pyroclastic Deposits from LRO Diviner Data

Lunar pyroclastic deposits (LPDs) are low albedo features that mantle underlying terrain (Gaddis et al. 1985). They are high priority targets for science and exploration as they are believed to originate from and therefore reflect the composition of the deep lunar interior (NRC, 2011). They are also the best potential resource of oxygen out of any Apollo samples (Allen et al. 1996). Historically, LPDs have been divided into regional versus local categories (Gaddis et al. 2003). The large (>1000 km2 area) regional deposits are deeply sourced (>400 km deep) and result from fire fountaining. Small (<1000 km2) local deposits are thought to result from Vulcanian eruptions in which magma is slowly emplaced beneath the surface until enough volatiles exsolve and the high pressure causes an explosion. Bennett et al. (2013) identified a local deposit (674 km2 area) that may have resulted from both Vulcanian activity and fire fountaining. This deposit potentially represents a new intermediate class of LPDs that straddles the interface between the two formation mechanisms. The deposit also exhibits the highest inferred FeO wt.% of any known lunar glass. In this work we investigate the inferred FeO abundances of other medium-sized deposits to characterize this potential new class of deposits and understand the magnitude of variations in inferred FeO among pyroclastic deposits. We use the method of Greenhagen et al. (2010) to calculate the wavelength of the Christiansen Feature (CF) from Lunar Reconnaissance Orbiter Diviner Lunar Radiometer instrument thermal-infrared observations for four medium-sized deposits. From the CF values, we estimate each deposit's FeO abundance using the method of Allen et al. (2012). The four LPDs that we examined (Oppenheimer South, Beer, Cleomedes, and J. Herschel) all have average CF values from 8.22-8.28 microns, corresponding to FeO abundances of approx. 10-15 wt.%. All of these values are within the range and uncertainties of FeO abundances measured in Apollo samples. As previously identified, the Oppenheimer South deposit exhibits an area of enhanced CF values (8.49 microns) that, if the methods of Allen et al. (2012) can be extrapolated, correspond to a highest observed approx. 30 wt.% FeO. Moon Mineralogy Mapper near-infrared spectra indicate that this area is glass-rich as opposed to olivine-rich. While we are still investigating the nature of the high CF wavelength in Oppenheimer South, spatially-resolved observations there and (to a smaller degree) in our other study sites, shows that FeO wt.% can vary within LPDs. Thus, obtaining only the average FeO abundance over a large area may not be adequate to understand global variation. The magnitude of Oppenheimer South's CF variability, if due to actual surface variations rather than calibration artifacts or spectral mixing, could indicate that it is a unique deposit and not part of a new mid-sized class of deposits. The higher value could be a result of its location within the South Pole Aitken Basin and exsolution of more deeply sourced magma due to the thin crust there.

Bennett, K. A.↗